In this notebook we conduct exploratory factor analyses (EFAs) on the datasets for our studies of concepts of mental life, in which each participants judged the various mental capacities of a particular target entity. We analyze datasets for adults and children from each of our five field sites: the US, Ghana, Thailand, China, and Vanuatu.
This notebook contains the results presented in the supplement, in which we recode our three-point response scale to have two points (no = 0, kind of or yes = 1) and use tetrachoric correlations.
country n
US 127
Ghana 150
Thailand 150
China 136
Vanuatu 148
Total 711
the condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be used
country n
US 117
Ghana 150
Thailand 152
China 131
Vanuatu 143
Total 693
the condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be used
See All samples, below.
Saving 8 x 11.2 in image
Joining, by = "factor_A"
Joining, by = "factor_B"
Joining, by = "factor_A"
Joining, by = "factor_B"
the condition has length > 1 and only the first element will be usedVectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.the condition has length > 1 and only the first element will be usedVectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.the condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be used
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Joining, by = c("factor", "age_group", "country", "factor_name", "factor_descript", "factor_labdescript")
Joining, by = c("factor", "country", "age_group")
Column `country` joining character vector and factor, coercing into character vector
Saving 8 x 9.6 in image
Joining, by = c("factor", "age_group", "country", "factor_name", "factor_descript", "factor_labdescript")
Joining, by = c("factor", "country", "age_group")
Column `country` joining character vector and factor, coercing into character vector
Saving 8 x 9.6 in image
Joining, by = c("factor", "age_group", "country", "factor_name", "factor_descript", "factor_labdescript")
Joining, by = c("factor", "country", "age_group")
Column `country` joining character vector and factor, coercing into character vector
Saving 8 x 9.6 in image
Joining, by = c("factor", "age_group", "country", "factor_name", "factor_descript", "factor_labdescript")
Joining, by = c("factor", "country", "age_group")
Column `country` joining character vector and factor, coercing into character vector
Saving 8 x 9.6 in image
Joining, by = c("factor", "age_group", "country", "factor_name", "factor_descript", "factor_labdescript")
Joining, by = c("factor", "country", "age_group")
Column `country` joining character vector and factor, coercing into character vector
Saving 8 x 9.6 in image
Joining, by = c("factor", "age_group", "country", "factor_name", "factor_descript", "factor_labdescript")
Joining, by = c("factor", "country", "age_group")
Column `country` joining character vector and factor, coercing into character vector
Saving 13 x 7.8 in image